--- base_model: pawan2411/address-embedder datasets: [] language: [] library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:1175 - loss:CoSENTLoss widget: - source_sentence: 42/7 M.6, Bang Jak Subdistrict, Phra Pradaeng District, Samut Prakan Province 10130 sentences: - Phraeksa Road, Phraeksa Subdistrict, Mueang Samut Prakan District Samut Prakan Province, Thailand - 134/9 Suksawat Road, Pak Khlong Bang Pla Kot Subdistrict Teacher Phra Samut Chedi Samut Prakan Province, Thailand - No. 9, Village No. 6, Village - Soi Srinakarin Road, Thepharak Subdistrict, Mueang Samut Prakan District Samut Prakan Province, Thailand - source_sentence: 222/165 M.5 Mangkorn-Nakorndee Road, Praeksa Subdistrict, Praeksa District, Muang Samut Prakan Province, Samut Prakan 10280 sentences: - Samrong Tai Market (Khwai Khwai), Samut Prakan Province - Soi Thepharak 116, Phraeksa, Mueang Samut Prakan District, Samut Prakan Province, 10280, Thailand (in Soi Dragon-Na Light) - 499, Village No. 12, Bang Phli Yai Road, Bang Phli, Samut Prakan Province (Opposite Ruam Kanyu Foundation) - source_sentence: 109/1007 Moo 2, Mab Pruea, 28/1, Tambon Praeksa, Amphoe Mueang Samut Prakan, Samut Prakan Province 10280 sentences: - Pruksa Ville Village 66/2 House No. 109/379 Samut Prakan Province (in Pruksa Ville 66/2 Village Bang Na-Nam Daeng Soi 9, Soi 3, Ban corner intersection, right hand side) - Siam Night Market Samut Prakan Province (Siam Night Market) - Jongsiri Market, opposite Suthawi Village, Room 2 Samut Prakan Province (Tamru-Bang Phli Road, opposite Suthawi Village) - source_sentence: 889 M.6 PTT Praeksa Chokmanat Road, Praeksa Subdistrict, Praeksa District, Muang Samut Prakan Province, Samut Prakan 10280 sentences: - Building 451, Village No. 1, Bang Bo Subdistrict, Bang Bo District, Samut Prakan 10560 Building on Rattanaraj Road, Yellow Shop - 67 Pak Nam Subdistrict, Mueang Samut Prakan District, Samut Prakan, Thailand 10270, Samut Prakan Province (opposite the court) - PTT Oil Pump, Cho Kom Nat Phraeksa, Phraeksa Mai, Samut Prakan Samut Prakan Province (PTT Gas Station - source_sentence: 109 M.8, Soi Tesaban Bangpu 30, Phuttharacha Road, Thaiban Mai Subdistrict, Mueang Samut Prakan District, Samut Prakan Province 10280 sentences: - 76/3, Chakkapak Road, Pak Nam Mueang, Samut Prakan, Samut Prakan Province (Beside Seven in front of the hospital) - 9392 Tai Ban Mai, Mueang Samut Prakan District Samut Prakan Province, Thailand - 7/119 Village No. 2, Phraeksa Road, Ban Mai, Muang Samut Prakan District, Samut Prakan Province, 10280, Thailand (entering the first U -turn road --- # SentenceTransformer based on pawan2411/address-embedder This is a [sentence-transformers](https://www.SBERT.net) model for address embeddings. ## Model Details ### Model Description - **Model Type:** Sentence Transformer --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("pawan2411/address_net") # Run inference sentences = [ '60 Ratchadaphisek Rd, Khwaeng Khlong Toei, Khet Khlong Toei, Krung Thep Maha Nakhon 10110', '60 Ratchadaphisek Road, Krung Thep Maha Nakhon, Thailand', '61 Ratchadaphisek Road, Krung Thep Maha Nakhon, Thailand' ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) ``` ## Training Details ### Training Dataset ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `num_train_epochs`: 10 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.42.4 - PyTorch: 2.3.1+cu121 - Accelerate: 0.32.1 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ```